As 3D display technologies such as 3D TVs are now considered as a next major breakthrough in the ultimate visual experience of media, a demand for 3D content is rapidly increasing. The conversion of image data from 2D to 3D, a fast way to obtain 3D content from existing 2D content, has been extensively studied. One of the methods to convert 2D into 3D is to first generate a depth map, and then create left and right eye images from this depth map.
Nevertheless, most conventional 2D-to-3D image conversion technologies utilize the power of machine learning, which requires significant computing resources and processing time. These technologies involve segmenting the 2D image into super-pixels and recognizing each geometric and/or semantic region using information learned from training data, detecting vanishing lines, reconstructing depth information based on the segmentation or vanishing line detection, etc. Some of them also involve complicated high dimensional feature extraction, e.g. 646-dimensional feature vectors. All of these operations require complex computation and significant processing time, consume significant amount of computing resources, and thus are slow. These technologies may not be practical for real-time 2D-to-3D image conversion, especially on low-power computing devices and/or 3D display devices. In addition, many of these technologies only work for a limited range of images, for example, only working for motion pictures (e.g., a video) but not for a single still image.